Driving event data analysis

ABSTRACT

A driving analysis server may be configured to receive vehicle operation data from vehicle sensors, and may use the data to identify a potentially high-risk or unsafe driving event by the vehicle. The driving analysis server also may receive corresponding image data, video, or object proximity data from the vehicle or one or more other data sources, and may use the image, video, or proximity data to analyze the potentially high-risk or unsafe driving event. A driver score for the vehicle or driver may be calculated or adjusted based on the analysis of the data and the determination of one or more causes of the driving event.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is continuation of and claims priority toco-pending U.S. application Ser. No. 15/914,040, filed Mar. 7, 2018, andentitled “Driving Event Data Analysis,” which is a continuation of U.S.application Ser. No. 15/347,211 (now U.S. Pat. No. 9,947,217), filedNov. 9, 2016, and entitled “Driving Event Data Analysis,” which is acontinuation of and claims priority to U.S. application Ser. No.13/770,634 (now U.S. Pat. No. 9,535,878), filed Feb. 19, 2013 andentitled, “Driving Event Data Analysis,” which is a non-provisional ofand claims priority to U.S. Provisional Application No. 61/739,456,entitled “Driving Event Data Analysis,” filed Dec. 19, 2012, thecontents of which are incorporated herein by reference in their entiretyfor all purposes.

TECHNICAL FIELD

Aspects of the disclosure generally relate to the analysis of drivingdata and calculation of driver scores. In particular, various aspects ofthe disclosure include a framework for evaluating a driving event at avehicle using image data, video data, and object proximity data from thevehicle and other data sources.

BACKGROUND

Telematics includes the use of technology to communicate informationfrom one location to another. Telematics has been used for variousapplications, including for the exchange of information with electronicsensors. As telematics technology has progressed, various communicationmethodologies have been incorporated into automobiles and other types ofvehicles.

Telematics systems such as on-board diagnostics (OBD) systems may beused in automobiles and other vehicles. OBD systems may provideinformation from the vehicle's on-board computers and sensors, allowingusers to monitor a wide variety of information relating to the vehiclesystems, such as engine RPM, emissions control, coolant temperature,vehicle speed, timing advance, throttle position, and oxygen sensing,and many other types of data. Telematics devices installed withinvehicles may be configured to access the vehicle computers and sensordata, and transmit the data to a display within the vehicle, a personalcomputer or mobile device, or to a centralized data processing system.Data obtained from OBD systems has been used for a variety of purposes,including maintenance, diagnosis, and analysis.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure relate to methods, computer-readable media,and apparatuses for analyzing vehicle operation data, or driving data,and calculating or adjusting a driver score based on the analyzeddriving data. One or more computing devices within a vehicle, forexample, a telematics device, may be configured to collect vehicleoperational data and transmit the data to a vehicle operation computersystem or a driving analysis server. Vehicle operational data mayinclude various data collected by the vehicle's internal sensors,computers, and cameras, such as the vehicle's speed, rates ofacceleration, braking, or steering, impacts to the vehicle, usage ofvehicle controls, and other vehicle operational data. Based on thevehicle operational data, the driving analysis server may be configuredto identify one or more potentially high-risk or unsafe driving eventsat a vehicle, for example, an occurrence of sudden braking or swerving,an impact to the vehicle, speeding, or a moving violation, etc.Additional image data, video data, and/or object proximity data may bereceived from one or more data sources in order to analyze thepotentially high-risk or unsafe driving event. A driver score may becalculated or adjusted for one or more vehicles or drivers based on thedriving event and the analysis of the image, video, or proximity data.

In accordance with further aspects of the present disclosure, the image,video, or proximity data from the vehicle's internal cameras andsensors, internal cameras and sensors of other nearby vehicles, or imageand video data from traffic cameras or other data sources may be used todetermine one or more causes of the driving event. Data metrics may becalculated based on the image, video, and proximity data, and may becompared to one or more thresholds to determine causes of drivingevents. For example, a sudden occurrence of swerving or braking, animpact to the vehicle, or a moving violation may be caused by anobstruction in the path of the vehicle, adverse road conditions orweather conditions, poor visibility, an obstructed traffic sign orsignal, or an electrical or mechanical malfunction within the vehicle,etc. Additional potential causes of driving events include high-risk orunsafe driving behavior, such as speeding, tailgating, and distracteddrivers.

Other features and advantages of the disclosure will be apparent fromthe additional description provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIG. 1 illustrates a network environment and computing systems that maybe used to implement aspects of the disclosure.

FIG. 2 is a diagram of a driving analysis system, according to one ormore aspects of the disclosure.

FIG. 3 is a flow diagram illustrating an example method of adjusting adriver score based on a driving event at a vehicle using image data,video data, and/or object proximity data associated with the drivingevent, according to one or more aspects of the disclosure.

FIG. 4 is a flow diagram illustrating an example method of adjusting adriver score based on an occurrence of sudden braking, swerving, or avehicle impact, using associated image data, video data, and/or objectproximity data, according to one or more aspects of the disclosure.

FIG. 5 is a flow diagram illustrating an example method of adjusting adriver score based on an occurrence of a moving violation, usingassociated image data, video data, and/or object proximity data,according to one or more aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments of thedisclosure that may be practiced. It is to be understood that otherembodiments may be utilized.

As will be appreciated by one of skill in the art upon reading thefollowing disclosure, various aspects described herein may be embodiedas a method, a computer system, or a computer program product.Accordingly, those aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment or an embodiment combiningsoftware and hardware aspects. Furthermore, such aspects may take theform of a computer program product stored by one or morecomputer-readable storage media having computer-readable program code,or instructions, embodied in or on the storage media. Any suitablecomputer readable storage media may be utilized, including hard disks,CD-ROMs, optical storage devices, magnetic storage devices, and/or anycombination thereof. In addition, various signals representing data orevents as described herein may be transferred between a source and adestination in the form of electromagnetic waves traveling throughsignal-conducting media such as metal wires, optical fibers, and/orwireless transmission media (e.g., air and/or space).

FIG. 1 illustrates a block diagram of a computing device (or system) 101in communication system 100 that may be used according to one or moreillustrative embodiments of the disclosure. The device 101 may have aprocessor 103 for controlling overall operation of the device 101 andits associated components, including RAM 105, ROM 107, input/outputmodule 109, and memory 115. The computing device 101, along with one ormore additional devices (e.g., terminals 141, 151) may correspond to anyof multiple systems or devices, such as a driving analysis server orsystem, configured as described herein for receiving and analyzingvehicle driving data and calculating driver scores based on identifieddriving events.

Input/Output (I/O) 109 may include a microphone, keypad, touch screen,and/or stylus through which a user of the computing device 101 mayprovide input, and may also include one or more of a speaker forproviding audio output and a video display device for providing textual,audiovisual and/or graphical output. Software may be stored withinmemory 115 and/or storage to provide instructions to processor 103 forenabling device 101 to perform various functions. For example, memory115 may store software used by the device 101, such as an operatingsystem 117, application programs 119, and an associated internaldatabase 121. Processor 103 and its associated components may allow thedriving analysis system 101 to execute a series of computer-readableinstructions to receive driving data from a vehicle, identify a drivingevent based on the driving data, receive image data, video data, and/orobject proximity data associated with the driving event, and perform ananalysis of the driving event based on image data, video data, and/orobject proximity data.

The driving analysis system 101 may operate in a networked environment100 supporting connections to one or more remote computers, such asterminals 141 and 151. The terminals 141 and 151 may be personalcomputers, servers (e.g., web servers, database servers), or mobilecommunication devices (e.g., vehicle telematics devices, on-boardvehicle computers, mobile phones, portable computing devices, and thelike), and may include some or all of the elements described above withrespect to the driving analysis system 101. The network connectionsdepicted in FIG. 1 include a local area network (LAN) 125 and a widearea network (WAN) 129, and a wireless telecommunications network 133,but may also include other networks. When used in a LAN networkingenvironment, the driving analysis system 101 may be connected to the LAN125 through a network interface or adapter 123. When used in a WANnetworking environment, the system 101 may include a modem 127 or othermeans for establishing communications over the WAN 129, such as network131 (e.g., the Internet). When used in a wireless telecommunicationsnetwork 133, the system 101 may include one or more transceivers,digital signal processors, and additional circuitry and software forcommunicating with wireless computing devices 141 (e.g., mobile phones,vehicle telematics devices) via one or more network devices 135 (e.g.,base transceiver stations) in the wireless network 133.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, WiFi,and WiMAX, is presumed, and the various computing devices and drivinganalysis system components described herein may be configured tocommunicate using any of these network protocols or technologies.

Additionally, one or more application programs 119 used by the drivinganalysis server/system 101 may include computer executable instructions(e.g., driving analysis programs and driver score algorithms) forreceiving vehicle driving data, identifying driving events, retrievingadditional image data, video data, and/or object proximity dataassociated with the driving events, analyzing the driving events and theadditional associated data, performing driving data analyses or driverscore computations for one or more vehicles or drivers, and performingother related functions as described herein.

As used herein, a driver score (or driving score) may refer to ameasurement of driving abilities, safe driving habits, and other driverinformation. A driver score may be a rating generated by an insurancecompany, financial instruction, or other organization, based on thedriver's age, vision, medical history, driving record, and/or otheraccount data relating to the driver. For example, an insurance companyserver 101 may periodically calculate driver scores for one or more ofthe insurance company's customers, and may use the driver scores toperform insurance analyses and determinations (e.g., determine coverage,calculate premiums and deductibles, award safe driver discounts, etc.).As discussed below, the driver score may be increased or decreased basedon the real-time data collected by vehicle sensors, telematics devices,and other systems for measuring driving performance. For example, if adriver consistently drives within posted speed limits, wears a seatbelt,and keeps the vehicle in good repair, the driver score may be positivelyadjusted (e.g., increased). Alternatively, if a driver regularly speeds,drives aggressively, and does not properly maintain the vehicle, thedriver score may be negatively adjusted (e.g., decreased). It should beunderstood that a driver score, as used herein, may be associated withan individual, group of individuals, or a vehicle. For instance, afamily, group of friends or co-workers, or other group that shares avehicle, may have a single driver score that is shared by the group.Additionally, a vehicle may have an associated driver score that isbased on one or more primary drivers of the vehicle and can be affectedby the driving behavior of any the vehicle's drivers. In other examples,a vehicle may be configured to identify different drivers, and eachdriver of the vehicle may have a separate driver score.

FIG. 2 is a diagram of an illustrative driving analysis system 200. Eachcomponent shown in FIG. 2 may be implemented in hardware, software, or acombination of the two. Additionally, each component of the drivinganalysis system 200 may include a computing device (or system) havingsome or all of the structural components described above for computingdevice 101.

The driving analysis system 200 shown in FIG. 2 includes a vehicle 210,such as an automobile, motorcycle, or other vehicle for which a drivingevent data analysis may be performed and for which a driver score may becalculated. The vehicle 210 may include vehicle operation sensors 212capable of detecting and recording various conditions at the vehicle andoperational parameters of the vehicle. For example, sensors 212 maydetect and store data corresponding to the vehicle's speed, distancesdriven, rates of acceleration or braking, and specific instances ofsudden acceleration, braking, and swerving. Sensors 212 also may detectand store data received from the vehicle's 210 internal systems, such asimpact to the body of the vehicle, air bag deployment, headlights usage,brake light operation, door opening and closing, door locking andunlocking, cruise control usage, hazard lights usage, windshield wiperusage, horn usage, turn signal usage, seat belt usage, phone and radiousage within the vehicle, maintenance performed on the vehicle, andother data collected by the vehicle's computer systems.

Additional sensors 212 may detect and store the external drivingconditions, for example, external temperature, rain, snow, light levels,and sun position for driver visibility. Sensors 212 also may detect andstore data relating to moving violations and the observance of trafficsignals and signs by the vehicle 210. Additional sensors 212 may detectand store data relating to the maintenance of the vehicle 210, such asthe engine status, oil level, engine coolant temperature, odometerreading, the level of fuel in the fuel tank, engine revolutions perminute (RPMs), and/or tire pressure.

The vehicle 210 also may include one or more cameras and proximitysensors 214 capable of recording additional conditions inside or outsideof the vehicle 210. Internal cameras 214 may detect conditions such asthe number of the passengers in the vehicle 210, and potential sourcesof driver distraction within the vehicle (e.g., pets, phone usage,unsecured objects in the vehicle). External cameras and proximitysensors 214 may detect other nearby vehicles, traffic levels, roadconditions, traffic obstructions, animals, cyclists, pedestrians, andother conditions that may factor into a driving event data analysis.

The operational sensors 212 and the cameras and proximity sensors 214may store data within the vehicle 210, and/or may transmit the data toone or more external computer systems (e.g., a vehicle operationcomputer system 225 and/or a driving analysis server 220). As shown inFIG. 2, the operation sensors 212, and the cameras and proximity sensors214, may be configured to transmit data to a vehicle operation computersystem 225 via a telematics device 216. In other examples, one or moreof the operation sensors 212 and/or the cameras and proximity sensors214 may be configured to transmit data directly without using atelematics device 216. For example, telematics device 216 may beconfigured to receive and transmit data from operational sensors 212,while one or more cameras and proximity sensors 214 may be configured todirectly transmit data to a vehicle operation computer system 225 or adriving analysis server 220 without using the telematics device 216.Thus, telematics device 216 may be optional in certain embodiments whereone or more sensors or cameras 212 and 214 within the vehicle 210 may beconfigured to independently capture, store, and transmit vehicleoperation and driving data.

Telematics device 216 may be a computing device containing many or allof the hardware/software components as the computing device 101 depictedin FIG. 1. As discussed above, the telematics device 216 may receivevehicle operation and driving data from vehicle sensors 212, andproximity sensors and cameras 214, and may transmit the data to one ormore external computer systems (e.g., a vehicle operation computersystem 225 and/or a driving analysis server 220) over a wirelesstransmission network. Telematics device 216 also may be configured todetect or determine additional types of data relating to real-timedriving and the condition of the vehicle 210. In certain embodiments,the telematics device 216 may contain or may be integral with one ormore of the vehicle sensors 212 and proximity sensors and cameras 214discussed above, and/or with one or more additional sensors discussedbelow.

Additionally, the telematics device 216 may be configured to collectdata regarding the number of passengers and the types of passengers(e.g. adults, children, teenagers, pets, etc.) in the vehicle 210. Thetelematics device 216 also may be configured to collect data a driver'smovements or the condition of a driver. For example, the telematicsdevice 216 may include or communicate with sensors that monitor adriver's movements, such as the driver's eye position and/or headposition, etc. Additionally, the telematics device 216 may collect dataregarding the physical or mental state of the driver, such as fatigue orintoxication. The condition of the driver may be determined through themovements of the driver or through sensors, for example, sensors thatdetect the content of alcohol in the air or blood alcohol content of thedriver, such as a breathalyzer.

The telematics device 216 also may collect information regarding thedriver's route choice, whether the driver follows a given route, and toclassify the type of trip (e.g. commute, errand, new route, etc.). Incertain embodiments, the telematics device 216 may be configured tocommunicate with the sensors and/or cameras 212 and 214 to determinewhen and how often the vehicle 210 stays in a single lane or strays intoother lanes. To determine the vehicle's route, lane position, and otherdata, the telematics device 216 may include or may receive data from amobile telephone, a Global Positioning System (GPS), locational sensorspositioned inside a vehicle, or locational sensors or devices remotefrom the vehicle 210.

The telematics device 216 also may store the type of the vehicle 210,for example, the make, model, trim (or sub-model), year, and/or enginespecifications. The vehicle type may be programmed into the telematicsdevice 216 by a user or customer, determined by accessing a remotecomputer system, such as an insurance company or financial institutionserver, or may be determined from the vehicle itself (e.g., by accessingthe vehicle's 210 computer systems).

Vehicle operation computer system 225 may be a computing device separatefrom the vehicle 210, containing some or all of the hardware/softwarecomponents as the computing device 101 depicted in FIG. 1. The vehicleoperation computer system 225 may be configured to receive and store thevehicle operation data discussed above from vehicle 210, and similarvehicle operation data from one or more other vehicles 210 a-n. In theexample shown in FIG. 2, the vehicle operation computer system 225includes a vehicle operation database 227 that may be configured tostore the vehicle operation data collected from the vehicle sensors 212,proximity sensors and cameras 214, and telematics devices 216 of aplurality of vehicles. The vehicle operation database 227 may storeoperational sensor data, proximity sensor data, camera data (e.g.,image, audio, and/or video), location data and/or time data for multiplevehicles 210.

Data stored in the vehicle operation database 227 may be organized inany of several different manners. For example, a table in the vehicleoperation database 227 may contain all of the vehicle operation data fora specific vehicle 210, similar to a vehicle event log. Other tables inthe vehicle operation database 227 may store certain types of data formultiple vehicles. For instance, tables may store specific drivingbehaviors (e.g., driving speed, acceleration and braking rates,swerving, tailgating, use of seat belts, turn signals or other vehiclecontrols, etc.) for multiples vehicles 210 at specific locations, suchas specific neighborhoods, roads, or intersections. Vehicle operationdata may also be organized by time, so that the driving events orbehaviors of multiples vehicles 210 may be stored or grouped by time(e.g., morning, afternoon, late night, rush hour, weekends, etc.) aswell as location.

The system 200 also may include a driving analysis server 220,containing some or all of the hardware/software components as thecomputing device 101 depicted in FIG. 1. The driving analysis server 220may include hardware, software, and network components to receivevehicle operation data from the vehicle operation computer system 225and/or directly from a plurality of vehicles 210. The driving analysisserver 220 and the vehicle operation computer system 225 may beimplemented as a single server/system, or may be separateservers/systems. In some examples, the driving analysis server 220 maybe a central server configured to receive vehicle operation data from aplurality of remotely located vehicle operation computer systems 225.

As shown in FIG. 2, driving analysis server 220 may include a drivinganalysis module 221 and a driver score calculation module 222. Modules221 and 222 may be implemented in hardware and/or software configured toperform a set of specific functions within the driving analysis server220. For example, the driving analysis module 221 and the driver scorecalculation module 222 may include one or more driving eventanalysis/driver score calculation algorithms, which may be executed byone or more software applications running on generic or specializedhardware within the driving analysis server 220. The driving analysismodule 221 may use the vehicle operation data received from the vehicleoperation computer system 225 and/or additional image data, video data,and/or object proximity data perform driving event analyses for vehicles210. The driver score calculation module 222 may use the results of thedriving event analysis performed by module 221 to calculate or adjust adriver score for a driver of a vehicle 210 based on specific drivingevents. Further descriptions and examples of the algorithms, functions,and analyses that may be executed by the driving analysis module 221 andthe driver score calculation module 222 are described below in referenceto FIGS. 3-5.

To perform driving event analyses and driver score calculations, thedriving analysis server 220 may initiate communication with and/orretrieve data from one or more vehicles 210, vehicle operation computersystems 225, and additional computer systems 231-233 storing data thatmay be relevant to the driving event analyses and driver scorecalculations. For example, one or more traffic data storage systems 231,such as traffic databases, may store data corresponding to the amount oftraffic and certain traffic characteristics (e.g., amount of traffic,average driving speed, traffic speed distribution, and numbers and typesof accidents, etc.) at various specific locations and times. Trafficdata storage systems 231 also may store image and video data recorded bytraffic cameras various specific locations and times. One or moreweather data storage systems 232, such as weather databases, may storeweather data (e.g., rain, snow, sleet, hail, temperature, wind, roadconditions, visibility, etc.) at different locations and differenttimes. One or more additional driving databases/systems 233 may storeadditional driving data from one or more different data sources orproviders which may be relevant to the driving event analyses and/ordriver score calculations performed by the driving analysis server 220.Additional driving databases/systems 233 may store data regarding eventssuch as road hazards and traffic accidents, downed trees, power outages,road construction zones, school zones, and natural disasters that mayaffect the driving event analyses and/or driver score calculationsperformed by the driving analysis server 220. As discussed below inreference to FIGS. 3-5, the driving analysis server 220 may retrieve anduse data from databases/systems 231-233 to analyze and evaluate thedriving events identified from the driving data of vehicles 210.

FIG. 3 is a flow diagram illustrating an example method of performing adriving event analysis and adjusting driver score based on a drivingevent identified for a vehicle, using image data, video data, and/orobject proximity data associated with the driving event. This examplemethod may be performed by one or more computing devices (e.g. drivinganalysis server 220, vehicle operation computer system 225, and vehicletelematics device 216) in the driving analysis system 200.

The steps in the example method of FIG. 3 describe performing ananalysis to determine whether or not to adjust a driver score inresponse to a potentially high-risk or unsafe driving event (e.g., anoccurrence of sudden braking or swerving, an impact to the vehicle, or amoving violation, etc.), using image data, video data, and/or objectproximity data associated with the driving event. For instance, anoccurrence of sudden swerving or braking by a vehicle 210 may indicate ahigh-risk or unsafe driving behavior by a driver not paying attention tothe road and the vehicle's immediate surroundings. However, if the imagedata, video data, and/or object proximity data shows that a pedestrian,animal, other vehicle, or object quickly and unexpectedly moved into thepath of the vehicle 210, then an occurrence of sudden swerving orbraking by the vehicle 210 may indicate that the driver of the vehicle210 was paying proper attention and reacted appropriately to thesituation. As another example, speeding or ignoring a posted trafficsign may be considered a potentially high-risk or unsafe drivingbehavior, and drivers that speed excessively and commit frequent movingviolations may receive lower driver scores than other drivers. However,for a vehicle 210 speeding or committing a moving violation, image data,video data, and/or object proximity data may show that there was anexternal cause for the driving event (e.g., obscured traffic signs, roadobstructions, malfunctioning traffic lights, etc.), or may show that aminor speeding offense or moving violation should not be consideredhigh-risk or unsafe driving (e.g., based on road conditions, weather,visibility, etc.).

In step 301, a driving analysis server 220 may receive vehicle operationdata (or driving data) for a vehicle 210. As described above inreference to FIG. 2, the driving analysis server 220 may receive vehicleoperation data from one or more vehicle operation computer systems 225and/or directly from telematics devices 216 or other systems on vehicles210. The vehicle driving data may correspond to sensor data collected bysensors 212 and/or additional data collected by a telematics device 216or other systems within a vehicle 210. In addition to the vehicleoperation data, the driving analysis server 220 may receive location andtime information corresponding to the vehicle operation data in step301. Vehicle location data and time data may be received from the samesources as other vehicle operation data, or may be collected bydifferent data sources or generated by the driving analysis server 220.For example, the driving analysis server 220 may receive vehicleoperation data from a vehicle operation system 225, and then mayinitiate communication with the vehicle's telematics device 216, GPSservers, time servers, or other systems to determine the location andtime that correspond to the received vehicle operation data.

In certain embodiments, telematics devices 216, vehicle operationsystems 225, and other data sources may transmit vehicle operation datafor a vehicle 210 to the driving analysis server 220 in real-time (ornear real-time). The driving analysis server 220 may be configured toreceive the vehicle operation data, and then perform real-time (or nearreal-time) driving analyses and driver score calculations for thevehicle 210. In other embodiments, vehicle operation data might not betransmitted in real-time but may be sent periodically (e.g., hourly,daily, weekly, etc.) by telematics devices 216 or vehicle operationsystems 225. Periodic transmissions of vehicle operation data mayinclude data for a single vehicle or single driver, or for multiplevehicles or drivers. The driving analysis server 220 may be configuredto receive the periodic transmissions, and then to perform periodicdriving event analyses and driver score calculations for one or morevehicles and drivers.

In step 302, the driving analysis server 220 may identify one or morepotentially high-risk or unsafe driving events (or driving behaviors)within the operation data of the vehicle 210. The driving eventsidentified in step 302 may correspond to specific occurrences orpatterns of high-risk, unsafe, or illegal driving activities that havethe potential to affect the driver score of the vehicle 210 or a driverof the vehicle 210.

For certain such driving events, the driving analysis server 220 mayidentify the driving event by analyzing only the vehicle operation data,for example, occurrences of sudden braking, accelerating, or swerving.Other driving events that may be identified based only on the vehicleoperation data include failure to use seatbelts, phone usage whiledriving, loud noise levels inside the vehicle while driving (e.g., highstereo volume or passenger noises), or other distractions in the vehicle(e.g., animated passengers or pets in the vehicle, eating while driving,texting while driving, etc.). Additionally, impacts to the body of thevehicle 210 (e.g., minor accidents, driving fast over speed bumps ordips, etc.) may be identified based only on the operation data receivedfor the vehicle 210.

To identify other types of driving events, the driving analysis server220 may analyze the vehicle operation data, as well as additional dataretrieved from one or more external data sources. For example, toidentify an occurrence of speeding by the vehicle 210, the drivinganalysis server 220 may receive vehicle speed data from a telematicsdevice 216 or vehicle operation system 225, and may receive thevehicle's location from the telematics device 216, vehicle operationsystem 225, or a separate GPS system associated with the vehicle 210.Then, the driving analysis server 220 may access a speed limit databaseto determine the legal speed limit at the location of the vehicle 210,and may compare the speed limit to the detected speed of the vehicle.The driving analysis server 220 may identify other moving violationsusing similar techniques. For example, the driving analysis server 220may identify a failure to use proper turn signals by analyzing the turnsignal usage of the vehicle 210, as compared to the location/drivingroute of the vehicle. Stop sign violations, illegal turns, and U-turnviolations may be identified by comparing the driving route of thevehicle 210 to a database of the traffic regulations and posted trafficsigns at different streets and intersections along the driving route.

Additional driving behaviors that may be identified in step 302 includeoccurrences of risky or aggressive driving under adverse drivingconditions or in safe driving areas. For example, it may be deemedhigh-risk or unsafe to drive a vehicle 210 at the maximum speed limitduring a rainstorm, snowstorm, or on slick or icy road conditions. Todetect an occurrence of this type of driving event, the driving analysisserver 220 may analyze the speed of the vehicle 210, the speed limit atthe vehicle's location, and the weather or road conditions at the timethe vehicle was being driven at that location. Additionally, it may bedeemed high-risk or unsafe to drive aggressively in safe driving areas,such as construction zones and school zones. To identify an occurrenceof aggressive driving in a safe driving area, the driving analysisserver 220 may analyze certain vehicle operation data (e.g., suddenacceleration or braking, phone usage while, other driver distractions,etc.), and compare the vehicle's location to a database of safe drivingareas.

In step 303, the driving analysis server 220 may retrieve additionalimage data, video data, and/or object proximity data associated with thedriving event(s) identified for the vehicle 210 in step 302. Asdiscussed below, the image, video, and proximity data retrieved in step303 may allow the driving analysis server 220 to identify externalcauses and/or justifications for potentially high-risk or unsafe drivingevents or behaviors. For example, if the driving event identified instep 302 is an occurrence of sudden braking or swerving by the vehicle210, or an impact to the vehicle 210, then in step 303 the drivinganalysis server 220 may retrieve image, video, and object proximity datafor the time and location of the event in order to determine if therewas an external cause for the swerving, braking, or vehicle impact.

The image, video, and object proximity data received by the drivinganalysis server 220 in step 303 may correspond to various differenttypes of cameras or sensors, and may be received from one or moredifferent data sources. For example, the image, video, and objectproximity data may include data recorded by the cameras and proximitysensors 214 of same vehicle 210 at which the driving event wasidentified in step 302. The data received in step 303 also may includedata recorded by cameras and proximity sensors 214 in one or more othervehicles 210 a-n that were in the immediate proximity of the vehicle 210at the time of driving event. Additionally, the data received in step303 may include image, video, and proximity data from other datasources, such traffic camera data received from one or more trafficdatabases/systems 231.

In certain embodiments, the driving analysis server 220 may identify apotentially high-risk or unsafe driving event of the vehicle 210 inreal-time or near real-time (in step 302), and may retrieve image,video, or object proximity data associated with the driving event ornear the same time (in step 303). For example, after identifying apotentially high-risk or unsafe driving event at the vehicle 210, one ormore of the telematics devices 216 of the vehicle, the vehicle operationsystem 225, or the driving analysis server 220 may be used to identifythe cameras and proximity sensors 214 of the vehicle 212 or other nearbyvehicles 210 a-n, or images and video feeds from traffic cameras in thevicinity of the vehicle 210 at the time of the identified driving event.GPS systems or proximity sensors within the vehicle 210 and/or vehiclelocation data from other sources may be used to identify traffic camerasor other data sources for image, video, and object proximity data in thevicinity of the vehicle 210 at the time of the driving event.Alternatively, the cameras or proximity sensors near the vehicle 210 atthe time of the driving event may be identified at a later time, basedon previously stored location data (e.g., GPS coordinates, driving routedata) and time data for the vehicle 210, one or more other vehicles 210a-n, and traffic cameras and other data sources 231.

In some instances, the driving analysis server 220 may already have, ormay already be configured to receive, the associated image data, vehicledata, and object proximity data from the vehicle cameras and proximitysensors 214 of the vehicle 210. For example, the driving analysis server220 may receive a stream or batch of vehicle operation data from one ormore vehicles 210 a-n or vehicle operation systems 225 containing all ofthe data recorded by all vehicle operation sensors 212, including thecameras and proximity sensors 214, in the set of vehicles 210 a-n. Thus,the driving analysis server 220 may receive the associated image, video,and proximity data for a vehicle 210 at the time same that it receivesthe speed, braking, acceleration, and steering data, and other sensordata from which it may identify a driving event. In other instances, thedriving analysis server 220 may specifically request image, video, orobject proximity data from the vehicle operation system 225, vehicletelematics devices 216, or other data sources corresponding to the timeand location of the driving event, in response to identifying thedriving event for the vehicle 210. Thus, the driving analysis server 220may be configured to locate, initiate communication with, and requestspecific sensor/camera data from vehicle operation systems 225, vehicletelematics devices 216, and/or traffic camera data sources 231corresponding to the time and location of the driving event.

As an example, if the driving event identified in step 302 is an impactto the front of a first vehicle 210, then the driving analysis server220 may be configured to specifically request and retrieve image, video,and object proximity data from the front-facing cameras and sensors 214of first vehicle 210. Additionally, the driving analysis server 220 mayuse the location and time data associated with the driving event toidentify one or more other vehicles 210 a-n in the vicinity of the firstvehicle 210 at the time of the driving event. For instance, the drivinganalysis server 220 may access GPS system data, location and time datafrom telematics devices 216, or location and time data from vehicleoperation systems 225 for a plurality of other vehicles. Afteridentifying one or more other vehicles 210 a-n near the first vehicle210 at the time of the driving event, the driving analysis server 220may request and retrieve image, video, and object proximity data fromthe relevant cameras and sensors 214 of those vehicles. For instance,data from rear-facing cameras and proximity sensors may be requestedfrom vehicles that were in front of the first vehicle 210 at the time ofthe driving event, data from front-facing cameras and proximity sensorsmay be requested from vehicles that were behind the first vehicle 210 atthe time of the driving event, etc. The driving analysis server 220 alsomay use the location and time data associated with the driving event toidentify one or more traffic cameras or other traffic data sources thatmay have recorded image, video, or proximity data in the vicinity of thefirst vehicle 210 at the time of the driving event (e.g., trafficcameras at the road or intersection where the driving event occurred).The driving analysis server 220 may request and retrieve traffic cameraimages or video recordings from one or more traffic data sources 231corresponding to the time and location of the driving event.

In step 304, the driving analysis server 220 may analyze the image,video, and/or object proximity data retrieved in step 303 to determineone or more causes of the driving event identified in step 302. Forexample, if the identified driving event is an occurrence of suddenbraking or swerving, or an impact to a vehicle 210, then the image,video, and/or object proximity data may be analyzed to determine thatthe braking, swerving, or impact was caused by one or more of: anobstruction in the path in the vehicle 210; adverse road conditions; anelectrical or mechanical malfunction within the vehicle; and/orhigh-risk or unsafe driving by the driver of the vehicle. As anotherexample, if the identified driving event is an occurrence of speeding oranother type of moving violation, then the image, video, and/or objectproximity data may be analyzed to determine that the speeding or movingviolation was caused by one or more of: a disabled vehicle or other roadobstruction, a road construction zone, an obscured or missing trafficsign, and/or a malfunctioning traffic signal. FIGS. 4 and 5, discussedbelow, respectively describe examples of driving event analyses anddeterminations of a driver score adjustments based on a braking,swerving, or vehicle impact driving event (FIG. 4), or based on aspeeding or moving violation driving event (FIG. 5).

To determine a cause for a driving event in step 304, the drivinganalysis server 220 may analyze and measure the image, video, and/orproximity data retrieved in step 303, calculate relevant data metricsrelating to the event, and compare the data metrics to predeterminedthresholds. For example, image, video, and/or proximity data may showthat a vehicle 210 swerved to avoid hitting an object that was in thevehicle's path (e.g., a pedestrian, cyclist, animal, disabled vehicle,etc.), and then hit another object (e.g., a tree or a parked car) as aresult. In this case, the driving analysis server 220 may analyze theimage, video, and/or proximity data to determine the distance betweenthe object and the vehicle when the object entered the vehicle's path,or the distance at which the object was first visible to the driver ofthe vehicle 210. From this distance, the driving analysis server 220 mayuse the vehicle's speed to calculate the amount of time that the driverhad to react to the obstruction, and may compare that amount of time toa reaction time threshold. For instance, if the driver had very littletime to react to the obstruction (e.g., (0-2 seconds), the drivinganalysis server 220 may determine that the obstruction was the cause ofthe swerving/accident event, rather than any unsafe or high-risk drivingon the part of the driver. On the other hand, if the driver had ampletime (e.g., 5-10 seconds) to observe and avoid the obstruction, but onlyswerved at the last second (hitting the parked car or tree), the drivinganalysis server 220 may determine that the obstruction was not the causeof the swerving/accident event.

In addition to calculating a reaction time data metric and comparing itto a reaction time threshold, as described in the example above, thedriving analysis server 220 may calculate and use additional datametrics and thresholds for determining the causes of driving events. Asanother example, after an accident between two vehicles, the drivinganalysis server 220 may use the image, video, and/or proximity data todetermine the following distance of the trailing vehicle just before theaccident. Based on the following distance, the driving analysis server220 may use the speed of the vehicles to calculate a tailgating timemetric for the trailing vehicle, which may be compared to a tailgatingsafety threshold (e.g., 2 seconds) to determine whether or nottailgating by the trailing vehicle will be classified as a cause of theaccident. Additional data metrics relating to weather conditions, roadconditions, and visibility may be calculated using image data and videodata received in step 303, and may be used in combination with otherdata metrics to determine causes of driving events. For instance, afirst tailgating safety threshold (e.g., 2 seconds) may be applied ingood weather and high visibility conditions, while a second tailgatingsafety threshold (e.g., 5 seconds) may be applied in poor weather andlow visibility conditions such as rain, fog, icy roads, etc. After atraffic accident, if the analysis of the image, video, and proximitydata indicates that the trailing vehicle was tailgating closer than thetailgating safety threshold for the current road/weather/visibilityconditions, then the driving analysis server 220 may determine thattailgating by the trailing vehicle was a cause of the accident.

As another example, a first speeding safety threshold (e.g., 10 MPH overthe posted speed limit), a first turn signal safety threshold (e.g., 100feet before turning), and/or a first braking safety threshold (e.g., 80%of maximum braking pressure) may be applied for daytime driving on goodroad conditions, while a second speeding safety threshold (e.g., 5 MPHunder the posted speed limit), a second turn signal safety threshold(e.g., 250 feet before turning), and/or a second braking safetythreshold (e.g., 40% of the maximum braking pressure) may be applied forspecific roads/locations or specific ranges of adverse road conditions(e.g., low visibility ranges, poor road traction ranges, etc.). Inaddition to these examples, other types of safe/unsafe drivingthresholds (e.g., vehicle speed thresholds, vehiclemaintenance/operational condition thresholds, tailgating thresholds,lane change thresholds, lane departure thresholds, reaction timethresholds, weather condition thresholds, road condition thresholds,traffic condition thresholds, road visibility thresholds, and thresholdsrelating to the driver distractions and the use of vehicle controls,etc.) may be used in the analyses of step 304.

The determination in step 304 may identify a single cause or multiplecauses for a driving event. As illustrated in the examples above, adriving event such as a sudden occurrence of swerving or braking, or animpact to the vehicle, may have multiple causes. Additionally, a movingviolation may have multiple possible causes, for example, a traffic signthat is blocked by an obstruction, bad road conditions, a roadconstruction project, and/or an impatient or high-risk driver. Thus, thedriving analysis server 220 may identify multiple causes for a drivingevent in step 304. In certain examples, the driving analysis server 220may use the calculated driving metrics and thresholds to calculate ranksand/or percentages associated with one or more determined causes of adriving event. For instance, a traffic accident between two vehicles maybe determined by the driving analysis server 220 to have been 30% causedby tailgating by the trailing vehicle, 15% caused by unexpectedly slickroad conditions, 5% caused by old and worn brake pads in the trailingvehicle 210, and 50% caused by a sudden occurrence of jaywalking by apedestrian in front of the leading vehicle.

In step 305, the driving analysis server 220 may determine if the cause(or one of the causes) of the driving event identified in step 302 washigh-risk or unsafe driving by the driver of the vehicle 210. In thisexample, if high-risk or unsafe driving by the driver of the vehicle 210was a cause of the driving event (305:Yes), then the driving analysisserver 220 may negatively adjust a driver score associated with thevehicle 210 or a driver of the vehicle 210 in step 306. On the otherhand, if high-risk or unsafe driving by the driver of the vehicle 210was not a cause of the driving event (306:No), then the driving analysisserver 220 might not adjust the driver score associated with the vehicle210 or driver in step 307.

As discussed above, the analysis in step 304 may determine a singlecause or multiple causes for a driving event, and may calculate ranks orpercentages associated with each determined cause. Thus, thedetermination performed by the driving analysis server 220 in step 305may take into account each of determined causes and the associated ranksor percentages, in deciding whether or not to adjust a driver score andby how much. For example, the driving analysis server 220 may negativelyadjust a driver score only if unsafe or high-risk driving was a primarycause of the driving event, or was greater than a predetermined eventcause threshold (e.g., >30% cause, >40% cause, >50% cause, etc.). In thecase of a driving event affecting two vehicles 210 a and 210 b, thedriving analysis server 220 may receive and analyze the data asdescribed above, and then may adjust a driver score associated with oneor both of the vehicles 210 a and 210 b. For example, an analysis of theimage, video, and proximity data for an accident between a first vehicle210 a and a second vehicle 210 b may show that the accident was causedpartially by erratic and distracted driving by the first driver invehicles 210 a, and partially by tailgating too closely by the seconddriver in vehicle 210 b, resulting in vehicle 210 b rear-ending vehicle210 a. In this case, the driving event may be analyzed and driver scoresfor both vehicles may be adjusted accordingly.

Although the example of FIG. 3 shows a negative driver score adjustment(step 306), in other examples a driver score may be positively adjustedbased on the image, video, and proximity data analysis associated withthe driving event. For instance, if an image, video, and proximity dataanalysis in step 304 indicates that a driver was not speeding ortailgating based on the current road conditions, and that the driverreacted quickly and appropriately to an unforeseen driving event (e.g.,by quickly swerving or braking to avoid a pedestrian), then the driverscore may be positively adjusted (e.g., raised) based on the drivingevent. Both positive and negative driver score adjustments may be madein varying degrees/magnitudes based on the image, video, and proximitydata analysis in step 304. For instance, the driver scores forvehicles/drivers performing in an exceptionally competent and safemanner may be positively adjusted by a greater amount, and vice versa.The magnitude of the driver score adjustments may be based on themagnitude of the data metrics calculated (e.g., speed of driver reactiontime, degree of tailgating, degree of speeding, degree of poor drivingconditions, degree of low visibility, degree of driver distractions,etc.), and may be determined using additional threshold comparisons instep 304.

In the above examples, the identification of driving events (steps301-302), retrieval of image, video, and proximity data (step 303),driving event analyses (step 304), and driver score determinations(steps 305-307), may be performed by a driving analysis server 220external to the vehicle 210. In other examples, a driving analysisserver 220 (or other devices) may be located partially or entirelywithin a vehicle 210 and may be configured to perform some or all of thesame steps described above in steps 301-307. For instance, one or moredevices within the vehicle 210 (e.g., a telematics device 216 or adriver's mobile device) may be configured as a driving analysis server220. In certain examples, a driving analysis server 220 within thevehicle 210 may be configured to report the driving event to a vehicleoperation system 225 or other external system (e.g., an insurancecompany computer system) only if a determined cause of the driving eventwas high-risk or unsafe driving, but not if the determined causes of thedriving event were something other than high-risk or unsafe driving.

Referring now to FIGS. 4 and 5, two examples are shown of driving eventanalyses and determinations of a driver scores based on an occurrence ofbraking, swerving, or a vehicle impact (FIG. 4), or based on a movingviolation (FIG. 5). As illustrated in these examples, there may bedifferences in the types of image, video, or object proximity datareceived, the data analyses performed, driving event causes identified,and the driver score adjustments determined, based on the specific typeof driving event and the specific circumstances and data available foreach driving event.

Referring to FIG. 4, in step 401 a driving analysis server 220 mayreceive vehicle operation data (or driving data) for a vehicle 210, forexample, from a data telematics device 216 or a vehicle operationcomputer system 225, using various techniques as described above inreference to step 301. In step 402, the driving analysis server 220 mayanalyze the received vehicle operation data and may identify anoccurrence of a sudden swerving, sudden braking, or vehicle impact eventby the vehicle 210, using various techniques as described above inreference to step 302. The driving analysis server 220 also maydetermine the time and location of driving event. In step 403, thedriving analysis server 220 may retrieve image data, video data, and/orproximity data associated with the sudden swerving, sudden braking, orvehicle impact by the vehicle 210. For example, the driving analysisserver 220 may use the time and location of the driving event, and mayrequest and retrieve the associated data from one or more data sources,such as the appropriate cameras and proximity sensors 214 of the vehicle210, other cameras and proximity sensors 214 in one or more additionalvehicles 210 a-201 n, and/or images and video from traffic cameras andother traffic data sources 231 corresponding to the time and location ofthe driving event, as described above in reference to step 303.

In step 404, the driving analysis server 220 may analyze the retrievedimage, video, and/or proximity data and may attempt to identify anexternal cause for the sudden swerving, sudden braking, or vehicleimpact by the vehicle 210. For example, an image or video analysis bythe driving analysis server 220 of the front-facing camera data of thevehicle 210, or other image or video data, may indicate that apedestrian, animal, cyclist, disabled vehicle, or other obstruction wasan external cause of the sudden swerving, braking, or impact by thevehicle 210. If an external cause cannot be identified (404:No), thenthe driver's score in this example may be negatively adjusted in step408, using various techniques as described above in reference to step306. If an external cause for the sudden swerving, sudden braking, orvehicle impact can be identified (404:Yes), then the driving analysisserver 220 may retrieve and analyze additional data in step 405 todetermine the driving behavior and driving profile of the driver/vehicleprior to the swerving, braking, or impact. Thus, in step 405, additionalimage, video, and/or proximity data, along with other types of data fromvarious data sources, may be retrieved to determine if thedriver/vehicle was driving in a competent and safe manner prior to theoccurrence of the swerving, braking, or impact to the vehicle. Forinstance, the driving analysis server 220 may determine in step 405 ifthe vehicle 210 was tailgating (using one or more tailgatingthresholds), speeding (using one or more speed thresholds), drivingimpatiently or recklessly (using one or more braking and accelerationrate thresholds, lane change or lane departure thresholds, etc.), ordriving while distracted (using one or more reaction time thresholds,noise threshold, and/or driver distraction thresholds, etc.), and so on.The analysis in step 405 also may be based on a driver profile for thevehicle 210 or a driver of the vehicle 210. Driver profiles may becalculated and stored, for example, by an insurance company, financialinstitution, or other organization, using a driver's age, drivingrecord, and the driver's previous scores and behaviors associated withprevious driving events.

If the driving analysis server 220 determines, based on the previousdriving behavior and driver profile, that the vehicle was being drivensafely prior to and during the swerving, braking, or impact to thevehicle (405:Safe), then the driver's score may be positively adjustedin step 406. On the other hand, if the driving analysis server 220determines, based on the previous driving behavior and driver profile,that the vehicle was not being driven safely prior to and during theswerving, braking, or vehicle impact (405:Unsafe), then the driver'sscore may be negatively adjusted in step 408. If the driving analysisserver 220 determines, based on the previous driving behavior and driverprofile, that the vehicle was being driven within an average range ofsafe driving behavior prior to and during the swerving, braking, orvehicle impact (405:Average), then the driver's score might not beadjusted positively or negatively in this example (step 407).

Referring now to FIG. 5, in step 501, a driving analysis server 220 mayreceive vehicle operation data (or driving data) for a vehicle 210,using various techniques as described above in reference to steps 301and 401. In step 502, the driving analysis server 220 may analyze thereceived vehicle operation data and identify an occurrence of a movingviolation (e.g., speeding, a stop sign violation, an illegal turn, aU-turn violation, or a failure to use turn signals or required othervehicle controls, etc.), using various techniques as described above inreference to step 302. For example, the driving analysis server 220 mayidentify a failure to use proper turn signals by analyzing the turnsignal usage of the vehicle 210, as compared to the location/drivingroute of the vehicle. Speeding, stop sign violations, illegal turns, andU-turn violations may be identified by comparing the driving route ofthe vehicle 210 to a database of the traffic regulations and postedtraffic signs at different roads and intersections along the drivingroute. The driving analysis server 220 also may determine the time andlocation of driving event identified in step 502.

In step 503, the driving analysis server 220 may analyze the vehicleoperation data to determine a level of severity of the speeding ormoving violation. To determine a level of severity, the driving analysisserver 220 may compare the vehicle operation data to one or moreseverity thresholds for speeding or moving violations. For example, aspeeding severity threshold of 10 MPH over the speed limit may beapplied to classify occurrences of speeding between 0-10 MPH over thespeed limit as “Minor” and occurrences of speeding greater than or equalto 10 MPH over the speed limit as “Severe.” As another example, a movingviolation for failing to come to a complete stop at a stop sign may havea severity threshold corresponding to the vehicle's speed when passingthe stop sign (e.g., “Minor”=less than 5 MPH, “Severe”=greater than 5MPH). Additional severity thresholds may be stored and used by thedriving analysis server 220 for additional types of moving violations.In certain cases, the severity thresholds for moving violations mayrelated to the number of times or frequency that a vehicle commits themoving violation. For example, one instance of failing to use a turnsignal when turning or changing lanes may be considered a “Minor”violation, while repeatedly failing to do so (e.g., greater than 5 timesin one trip, greater than 25% of the time, etc.) may be classified as a“Severe” turn signal violation.

If the moving violation(s) identified in step 502 are determined to be“Severe” in step 503 (503:Severe), then driving analysis server 220 maynegatively adjust the driver's score in step 507. Thus, the drivinganalysis server 220 in this example may adjust the driver's score basedon “Severe” occurrence of speeding or a moving violation, withoutretrieving or analyzing any image data, video data, and/or proximitydata associated with the speeding or moving violation. In otherexamples, image data, video data, and/or proximity data may be receivedand analyzed in all cases. Additionally, rather than classifyingoccurrences moving violations into only two categories, many differentcategories may be defined corresponding to the severity/magnitude,frequency, and external conditions (e.g., weather, road conditions,driver distractions) associated with the moving violation. In suchcases, the different categories of severity may be processed differentlyby the driving analysis server 220, for instance, using differentmagnitudes of adjustments to the driver's score in step 507, or byretrieving and analyzing different sets of image, video, and/orproximity data using different algorithms, as described in the examplesabove.

In this example, if the moving violation(s) identified in step 502 aredetermined to be “Minor” in step 503 (503:Minor), then driving analysisserver 220 retrieve image data, video data, and/or proximity dataassociated with the moving violation event by the vehicle 210. Forinstance, the driving analysis server 220 may use the time and locationof the driving event, and may request and retrieve the associated datafrom one or more data sources, such as the appropriate cameras andproximity sensors 214 of the vehicle 210, other cameras and proximitysensors 214 in one or more additional vehicles 210 a-201 n, and/orimages and video from traffic cameras and other traffic data sources 231corresponding to the time and location of the driving event, asdescribed above in reference to steps 303 and 403.

In step 505, the driving analysis server 220 may analyze the image,video, and/or proximity data retrieved in step 504 to determine whetheror not there was an external cause or a justification for the movingviolation. For example, analysis of the image, video, and/or proximitydata by the driving analysis server 220 may indicate that a movingviolation was caused by an obstruction (e.g., disabled vehicle, roadconstruction zone, or other obstruction) that required the vehicle 210to drive on the opposite side of the road or perform a driving maneuverthat would normally be considered a traffic violation. Additionally, ifan analysis of the image or video data shows that a traffic sign ismissing or obscured (e.g., by a tree, service vehicle, or other sign),or if a traffic signal is malfunctioning, then the driving analysisserver 220 may determine that the ineffective traffic sign or signal wasan external cause of the vehicle's 210 moving violation.

The driving analysis server 220 also may determine in step 505 when aminor occurrence of speeding or another moving violation is not actuallyunsafe or high-risk driving, and thus might not be deserving of anegative adjustment to the driver's score. To determine whether or not aminor moving violation is justified (i.e., not unsafe or high-risk), thedriving analysis server 220 may analyze the image, video, and/orproximity data retrieved in step 504, along with any other availabledata to evaluate the driving conditions and circumstances of the movingviolation. For example, if the minor moving violation took place in goodweather conditions, good road conditions, high visibility, and lowtraffic conditions, then the driving analysis server 220 may determinethat that a minor occurrence of speeding or another moving violation wasnot unsafe or high-risk (505:Yes), and might not negatively adjust thedriver's score as a result of the violation. In order to make thedetermination in step 505, data metrics relating to weather conditions,road conditions, and visibility may be calculated using image data andvideo data received in step 504, and/or data from additional datasources (e.g., traffic databases 231, weather databases 232, etc.) Thecalculated data metrics for weather conditions, road conditions,visibility, traffic, etc., may be compared to one or more predeterminedthresholds for safe driving conditions, for example, a minimum safe roadconditions threshold, a minimum safe visibility threshold, a maximumsafe traffic threshold, to determine whether the minor moving violationtook place in sufficiently safe driving conditions to classify theviolation as not unsafe or high-risk. On the other hand, if the minorviolation did not take place in good weather conditions, good roadconditions, high visibility, or low traffic conditions, then the drivinganalysis server 220 may determine that that the minor speeding or movingviolation was unsafe or high-risk (505:No), and may negatively adjustthe driver's score in step 507 as a result of the violation.

While the aspects described herein have been discussed with respect tospecific examples including various modes of carrying out aspects of thedisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above described systems andtechniques that fall within the spirit and scope of the invention.

The invention claimed is:
 1. A driving analysis system comprising afirst vehicle on-board data recording system and a driving analysisserver, wherein the first vehicle on-board data recording systemcomprises: one or more vehicle operation sensors configured to recordvehicle operation data at a first vehicle; and one or more telematicsdevices configured to transmit the vehicle operation data from the firstvehicle to the driving analysis server, wherein the driving analysisserver comprises a processor and a memory unit storingcomputer-executable instructions, which when executed by the processor,cause the driving analysis server to: receive a driver score specific tothe first vehicle; receive first vehicle operation data from the firstvehicle on-board data recording system; identify a first driving eventof the first vehicle based on the first vehicle operation data; identifyat least a first cause of the first driving event and a second cause ofthe first driving event, wherein identifying the at least a first causeand a second cause comprises: attempting to identify at least twoexternal causes of the first driving event by analyzing the firstvehicle operation data received from the first vehicle on-board datarecording system corresponding to the first driving event; identifying afirst external cause and a second external cause; based on the analysisof the first vehicle operation data received from the first vehicleon-board data recording system corresponding to the first driving event,determining a percentage of cause associated with each of the firstexternal cause and the second external cause; evaluating the determinedpercentage of cause of the first external cause and the second externalcause to identify one of the first external cause and the secondexternal cause as a primary cause, the primary cause being the causehaving a percentage greater than a predetermined event cause threshold;determining whether the primary cause was unsafe or high-risk driving;responsive to determining that the primary cause was unsafe or high-riskdriving, adjusting the driver score specific to the first vehicle;responsive to determining that the primary cause was not unsafe orhigh-risk driving, maintaining the driver score specific to the firstvehicle based on the first driving event; and output at least the firstcause of the first driving event.
 2. The driving analysis system ofclaim 1, wherein identifying the first driving event comprises:analyzing the first vehicle operation data to identify an occurrence ofsudden braking by the first vehicle or an occurrence of swerving by thefirst vehicle.
 3. The driving analysis system of claim 1, whereinidentifying the first driving event of the first vehicle by the drivinganalysis server comprises: determining an occurrence of a movingviolation by the first vehicle, based on the first vehicle operationdata.
 4. The driving analysis system of claim 3, wherein determining theoccurrence of the moving violation comprises: receiving moving vehicleregulation data from one or more external data sources, corresponding toa location and time of the first driving event; and comparing the firstvehicle operation data received from the first vehicle on-board datarecording system to the moving vehicle regulation data received from theone or more external data sources.
 5. The driving analysis system ofclaim 1, wherein attempting to identify at least two external causes ofthe first driving event by the driving analysis server comprises: usinga location and time of the first driving event, retrieving at least oneof traffic data, weather data, or road condition data corresponding tothe location and time of the first driving event, from one or moreexternal data sources; and analyzing the traffic data, weather data, orroad condition data retrieved from the one or more external datasources, along with the data received from the first vehicle on-boarddata recording system, to attempt to determine at least two externalcauses of the first driving event.
 6. The driving analysis system ofclaim 1, wherein identifying the first cause of the first driving eventcomprises: using the data received from the first vehicle on-board datarecording system to calculate a driver reaction time associated with thefirst driving event; and determining the at least a first cause of thefirst driving event based on the driver reaction time associated withthe first driving event.
 7. The driving analysis system of claim 1,wherein identifying the at least a first cause of the first drivingevent comprises: using the data received from the first vehicle on-boarddata recording system to calculate a driver visibility level associatedwith the first driving event; and determining the at least the firstcause of the first driving event based on the driver visibility levelassociated with the first driving event.
 8. The driving analysis systemof claim 1, wherein identifying the at least a first cause of the firstdriving event comprises: using the data received from the first vehicleon-board data recording system to calculate a traffic amount associatedwith the first driving event; and determining the at least a first causeof the first driving event based on the traffic amount associated withthe first driving event.
 9. The driving analysis system of claim 1, thedriving analysis server comprising further computer-executableinstructions, which when executed by the processor, cause the drivinganalysis server to: transmit a record of the first driving event to anexternal insurance system associated with the first vehicle only if theidentified at least two causes correspond to high-risk or unsafe drivingby a driver of the first vehicle, wherein if the at least two identifiedcauses of the first driving event correspond to something other thanhigh-risk or unsafe driving, the driving analysis server is configurednot to transmit a record of the first driving event to the externalinsurance system associated with the first vehicle.
 10. A drivinganalysis system comprising a first vehicle on-board data recordingsystem and a driving analysis server, wherein the first vehicle on-boarddata recording system comprises: one or more vehicle operation sensorsconfigured to record vehicle operation data at a first vehicle; and oneor more telematics devices configured to transmit the vehicle operationdata to the driving analysis server, wherein the driving analysis servercomprises a processor and a memory unit storing computer-executableinstructions, which when executed by the processor, cause the drivinganalysis server to: receive a driver score specific to the firstvehicle; receive first vehicle operation data from the first vehicleon-board data recording system; identify a first driving event of thefirst vehicle based on the first vehicle operation data; identify afirst location and time associated with the first driving event; receivedata corresponding to the first location and time associated with thefirst driving event; identify at least a first cause of the firstdriving event and a second cause of the first driving event, whereinidentifying the first cause and the second cause comprises: attemptingto identify at least two external causes of the first driving event byanalyzing the received vehicle operation data corresponding to the firstlocation and time associated with the first driving event; identifying afirst external cause and a second external cause; based on the analysisof the received data corresponding to the first location and timeassociated with the first driving event, determining a percentage ofcause associated with each of the first external cause and the secondexternal cause; evaluating the determined percentage of cause of thefirst external cause and the second external cause to identify one ofthe first external cause and the second external cause as a primarycause, the primary cause being the cause having a percentage greaterthan a predetermined event cause threshold; determining whether theprimary cause was unsafe or high-risk driving; responsive to determiningthat the primary cause was unsafe or high-risk driving, adjusting thedriver score specific to the first vehicle; responsive to determiningthat the primary cause was not unsafe or high-risk driving, maintainingthe driver score specific to the first vehicle based on the first causeof the first driving event; and output at least the first cause of thefirst driving event.
 11. The driving analysis system of claim 10,wherein receiving the data corresponding to the first location and timeassociated with the first driving event comprises: identifying one ormore additional vehicles at locations and times corresponding to thefirst location and time of the first driving event; and retrieving datacollected by at least one or more vehicle on-board data recordingsystems of the one or more additional vehicles at locations and timescorresponding to the first location and time of the first driving event.12. The driving analysis system of claim 10, wherein receiving the datacorresponding to the first location and time associated with the firstdriving event comprises: using the first location and time associatedwith the first driving event, identifying a first external trafficcamera in a vicinity of the first vehicle at the time of the firstdriving event; and retrieving data from the first external trafficcamera.
 13. The driving analysis system of claim 10, the drivinganalysis server comprising further computer-executable instructions,which when executed by the processor, cause the driving analysis serverto: transmit a record of the first driving event to an externalinsurance system associated with the first vehicle only if theidentified at least two causes correspond to high-risk or unsafedriving, wherein if the at least two identified causes of the firstdriving event corresponds to something other than high-risk or unsafedriving, the driving analysis server is configured not to transmit arecord of the first driving event to the external insurance systemassociated with the first vehicle.
 14. A method comprising: at a drivinganalysis server comprising a processor and memory unit storingcomputer-executable instructions, which, when executed by the processor,cause the driving analysis server to: receive a driver score specific toa first vehicle; receive first vehicle operation data from a firstvehicle on-board data recording system, the first vehicle on-board datarecording system including: one or more vehicle operation sensorsconfigured to record vehicle operation data at a first vehicle; and oneor more telematics devices configured to transmit the first vehicleoperation data from the first vehicle to the driving analysis server;identify a first driving event of the first vehicle based on the firstvehicle operation data; identify at least a first cause of the firstdriving event and a second cause of the first driving event, whereinidentifying the at least a first cause and a second cause comprises:attempting to identify at least two external causes of the first drivingevent by analyzing the first vehicle operation data received from thefirst vehicle on-board data recording system corresponding to the firstdriving event; identifying a first external cause and a second externalcause; based on the analysis of the first vehicle operation datareceived from the first vehicle on-board data recording systemcorresponding to the first driving event, determining a percentage ofcause associated with each of the first external cause and the secondexternal cause; evaluating the determined percentage of cause of thefirst external cause and the second external cause to identify one ofthe first external cause and the second external cause as a primarycause, the primary cause being the cause having a percentage greaterthan a predetermined event cause threshold; determining whether theprimary cause was unsafe or high-risk driving; responsive to determiningthat the primary cause was unsafe or high-risk driving, adjusting thedriver score specific to the first vehicle; responsive to determiningthat the primary cause was not unsafe or high-risk driving, maintainingthe driver score specific to the first vehicle based on the firstdriving event; and output at least the first cause of the first drivingevent.
 15. The method of claim 14, wherein identifying the first drivingevent comprises: analyzing the first vehicle operation data to identifyan occurrence of sudden braking by the first vehicle or an occurrence ofswerving by the first vehicle.
 16. The method of claim 14, whereinidentifying the first driving event of the first vehicle by the drivinganalysis server comprises: determining an occurrence of a movingviolation by the first vehicle, based on the first vehicle operationdata.
 17. The method of claim 16, wherein determining the occurrence ofthe moving violation comprises: receiving moving vehicle regulation datafrom one or more external data sources, corresponding to a location andtime of the first driving event; and comparing the first vehicleoperation data received from the first vehicle on-board data recordingsystem to the moving vehicle regulation data received from the one ormore external data sources.
 18. The method of claim 14, whereinattempting to identify at least two external causes of the first drivingevent by the driving analysis server comprises: using a location andtime of the first driving event, retrieving at least one of trafficdata, weather data, or road condition data corresponding to the locationand time of the first driving event, from one or more external datasources; and analyzing the traffic data, weather data, or road conditiondata retrieved from the one or more external data sources, along withthe data received from the first vehicle on-board data recording system,to attempt to determine at least two external causes of the firstdriving event.
 19. The method of claim 14, wherein identifying the firstcause of the first driving event comprises: using the data received fromthe first vehicle on-board data recording system to calculate a driverreaction time associated with the first driving event; and determiningthe at least a first cause of the first driving event based on thedriver reaction time associated with the first driving event.
 20. Themethod of claim 14, wherein identifying the at least a first cause ofthe first driving event comprises: using the data received from thefirst vehicle on-board data recording system to calculate a drivervisibility level associated with the first driving event; anddetermining the at least the first cause of the first driving eventbased on the driver visibility level associated with the first drivingevent.